Overview

Dataset statistics

Number of variables24
Number of observations1950
Missing cells10290
Missing cells (%)22.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory365.8 KiB
Average record size in memory192.1 B

Variable types

Numeric15
Categorical7
Unsupported2

Alerts

missing_value_latest has constant value ""Constant
missing_value_update has constant value ""Constant
time_type is highly imbalanced (99.4%)Imbalance
stderr_latest has 1594 (81.7%) missing valuesMissing
sample_size_latest has 1601 (82.1%) missing valuesMissing
computation_as_of_dt_latest has 1950 (100.0%) missing valuesMissing
stderr_update has 1594 (81.7%) missing valuesMissing
sample_size_update has 1601 (82.1%) missing valuesMissing
computation_as_of_dt_update has 1950 (100.0%) missing valuesMissing
time_value is highly skewed (γ1 = -44.14867533)Skewed
value_latest is highly skewed (γ1 = 30.19017055)Skewed
issue_update is highly skewed (γ1 = -44.15088754)Skewed
value_update is highly skewed (γ1 = 30.19017055)Skewed
computation_as_of_dt_latest is an unsupported type, check if it needs cleaning or further analysisUnsupported
computation_as_of_dt_update is an unsupported type, check if it needs cleaning or further analysisUnsupported
value_latest has 456 (23.4%) zerosZeros
value_update has 456 (23.4%) zerosZeros

Reproduction

Analysis started2024-04-08 02:46:38.212598
Analysis finished2024-04-08 02:46:49.118679
Duration10.91 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

signal_key_id
Real number (ℝ)

Distinct221
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.72564
Minimum1
Maximum831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-04-07T22:46:49.151672image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34
Q151
median70
Q396
95-th percentile391.55
Maximum831
Range830
Interquartile range (IQR)45

Descriptive statistics

Standard deviation119.38063
Coefficient of variation (CV)1.0979988
Kurtosis10.662265
Mean108.72564
Median Absolute Deviation (MAD)20.5
Skewness3.1480376
Sum212015
Variance14251.734
MonotonicityNot monotonic
2024-04-07T22:46:49.205798image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 171
 
8.8%
69 157
 
8.1%
68 136
 
7.0%
48 122
 
6.3%
73 116
 
5.9%
51 63
 
3.2%
66 53
 
2.7%
70 48
 
2.5%
65 48
 
2.5%
71 43
 
2.2%
Other values (211) 993
50.9%
ValueCountFrequency (%)
1 10
0.5%
2 7
0.4%
4 4
 
0.2%
7 1
 
0.1%
8 2
 
0.1%
9 2
 
0.1%
10 5
0.3%
12 3
 
0.2%
13 4
 
0.2%
14 2
 
0.1%
ValueCountFrequency (%)
831 1
0.1%
823 1
0.1%
821 1
0.1%
819 1
0.1%
765 1
0.1%
764 1
0.1%
679 2
0.1%
674 1
0.1%
672 1
0.1%
671 1
0.1%

geo_key_id
Real number (ℝ)

Distinct1513
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.9749
Minimum6
Maximum5406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-04-07T22:46:49.255488image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile172
Q1878.5
median1815
Q32768
95-th percentile4895.55
Maximum5406
Range5400
Interquartile range (IQR)1889.5

Descriptive statistics

Standard deviation1416.5606
Coefficient of variation (CV)0.70162369
Kurtosis-0.30595106
Mean2018.9749
Median Absolute Deviation (MAD)945
Skewness0.71075382
Sum3937001
Variance2006643.9
MonotonicityNot monotonic
2024-04-07T22:46:49.308641image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
684 6
 
0.3%
201 4
 
0.2%
2534 4
 
0.2%
2572 4
 
0.2%
3230 4
 
0.2%
809 4
 
0.2%
3083 3
 
0.2%
238 3
 
0.2%
2360 3
 
0.2%
8 3
 
0.2%
Other values (1503) 1912
98.1%
ValueCountFrequency (%)
6 1
 
0.1%
7 2
0.1%
8 3
0.2%
12 1
 
0.1%
14 2
0.1%
15 2
0.1%
18 2
0.1%
23 1
 
0.1%
26 1
 
0.1%
27 1
 
0.1%
ValueCountFrequency (%)
5406 2
0.1%
5402 1
0.1%
5401 1
0.1%
5398 1
0.1%
5397 1
0.1%
5386 1
0.1%
5382 1
0.1%
5381 1
0.1%
5336 1
0.1%
5335 1
0.1%

time_type
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
day
1949 
week
 
1

Length

Max length4
Median length3
Mean length3.0005128
Min length3

Characters and Unicode

Total characters5851
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowday
2nd rowday
3rd rowday
4th rowday
5th rowday

Common Values

ValueCountFrequency (%)
day 1949
99.9%
week 1
 
0.1%

Length

2024-04-07T22:46:49.354913image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T22:46:49.399780image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
day 1949
99.9%
week 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 1949
33.3%
a 1949
33.3%
y 1949
33.3%
e 2
 
< 0.1%
w 1
 
< 0.1%
k 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5851
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1949
33.3%
a 1949
33.3%
y 1949
33.3%
e 2
 
< 0.1%
w 1
 
< 0.1%
k 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5851
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1949
33.3%
a 1949
33.3%
y 1949
33.3%
e 2
 
< 0.1%
w 1
 
< 0.1%
k 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5851
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1949
33.3%
a 1949
33.3%
y 1949
33.3%
e 2
 
< 0.1%
w 1
 
< 0.1%
k 1
 
< 0.1%

time_value
Real number (ℝ)

SKEWED 

Distinct657
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20193929
Minimum202026
Maximum20220531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-04-07T22:46:49.507439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum202026
5-th percentile20200222
Q120200613
median20201021
Q320210401
95-th percentile20210902
Maximum20220531
Range20018505
Interquartile range (IQR)9788

Descriptive statistics

Standard deviation452994.35
Coefficient of variation (CV)0.022432204
Kurtosis1949.4033
Mean20193929
Median Absolute Deviation (MAD)603
Skewness-44.148675
Sum3.9378163 × 1010
Variance2.0520388 × 1011
MonotonicityNot monotonic
2024-04-07T22:46:49.560056image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200618 13
 
0.7%
20200622 11
 
0.6%
20200613 10
 
0.5%
20200611 9
 
0.5%
20200612 9
 
0.5%
20200623 8
 
0.4%
20200414 8
 
0.4%
20201002 8
 
0.4%
20200423 8
 
0.4%
20200527 8
 
0.4%
Other values (647) 1858
95.3%
ValueCountFrequency (%)
202026 1
0.1%
20190103 1
0.1%
20190105 1
0.1%
20190108 1
0.1%
20190111 1
0.1%
20190118 1
0.1%
20190120 1
0.1%
20190125 1
0.1%
20190126 1
0.1%
20190130 1
0.1%
ValueCountFrequency (%)
20220531 1
 
0.1%
20220530 1
 
0.1%
20220528 1
 
0.1%
20220527 3
0.2%
20220518 3
0.2%
20220512 1
 
0.1%
20220509 1
 
0.1%
20220504 1
 
0.1%
20220425 1
 
0.1%
20220416 1
 
0.1%

issue_latest
Real number (ℝ)

Distinct295
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20213328
Minimum20200514
Maximum20220822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-04-07T22:46:49.615832image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum20200514
5-th percentile20200830
Q120201229
median20220801
Q320220811
95-th percentile20220820
Maximum20220822
Range20308
Interquartile range (IQR)19582

Descriptive statistics

Standard deviation8315.2601
Coefficient of variation (CV)0.00041137511
Kurtosis-1.4326442
Mean20213328
Median Absolute Deviation (MAD)20
Skewness-0.46824495
Sum3.941599 × 1010
Variance69143551
MonotonicityNot monotonic
2024-04-07T22:46:49.670847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201030 153
 
7.8%
20220821 70
 
3.6%
20200825 67
 
3.4%
20220801 59
 
3.0%
20201014 58
 
3.0%
20220815 53
 
2.7%
20220807 51
 
2.6%
20220805 50
 
2.6%
20220802 50
 
2.6%
20220812 50
 
2.6%
Other values (285) 1289
66.1%
ValueCountFrequency (%)
20200514 2
 
0.1%
20200523 2
 
0.1%
20200620 5
 
0.3%
20200623 11
 
0.6%
20200701 1
 
0.1%
20200709 2
 
0.1%
20200710 5
 
0.3%
20200730 1
 
0.1%
20200825 67
3.4%
20200829 2
 
0.1%
ValueCountFrequency (%)
20220822 10
 
0.5%
20220821 70
3.6%
20220820 50
2.6%
20220819 43
2.2%
20220818 47
2.4%
20220817 29
1.5%
20220816 43
2.2%
20220815 53
2.7%
20220814 45
2.3%
20220813 46
2.4%

value_latest
Real number (ℝ)

SKEWED  ZEROS 

Distinct1288
Distinct (%)66.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean978.05284
Minimum-2.1428571
Maximum340882.01
Zeros456
Zeros (%)23.4%
Negative7
Negative (%)0.4%
Memory size15.4 KiB
2024-04-07T22:46:49.727855image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-2.1428571
5-th percentile0
Q10.040093664
median2.2548675
Q350.192292
95-th percentile5342.009
Maximum340882.01
Range340884.15
Interquartile range (IQR)50.152198

Descriptive statistics

Standard deviation9120.4606
Coefficient of variation (CV)9.3251205
Kurtosis1051.0108
Mean978.05284
Median Absolute Deviation (MAD)2.2548675
Skewness30.190171
Sum1907203
Variance83182801
MonotonicityNot monotonic
2024-04-07T22:46:49.779330image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 456
 
23.4%
1 29
 
1.5%
0.1428571 19
 
1.0%
0.1428571429 18
 
0.9%
2 17
 
0.9%
0.2857142857 10
 
0.5%
0.4285714 8
 
0.4%
0.7142857 8
 
0.4%
4 7
 
0.4%
5 7
 
0.4%
Other values (1278) 1371
70.3%
ValueCountFrequency (%)
-2.142857143 2
 
0.1%
-1.581327683 1
 
0.1%
-0.8571429 1
 
0.1%
-0.7113336795 1
 
0.1%
-0.2857143 1
 
0.1%
-0.2857142857 1
 
0.1%
0 456
23.4%
0.005062965267 1
 
0.1%
0.007039712802 1
 
0.1%
0.0160253 1
 
0.1%
ValueCountFrequency (%)
340882.0109 1
0.1%
167947.1874 1
0.1%
81852.71429 1
0.1%
30070.67938 1
0.1%
24169.42857 1
0.1%
22624 1
0.1%
18976 1
0.1%
17487.12805 1
0.1%
17082.48887 1
0.1%
16950.71396 1
0.1%

stderr_latest
Real number (ℝ)

MISSING 

Distinct356
Distinct (%)100.0%
Missing1594
Missing (%)81.7%
Infinite0
Infinite (%)0.0%
Mean3.4790508
Minimum0
Maximum83.606519
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-04-07T22:46:49.832685image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0017151554
Q10.01609597
median1.3661809
Q32.9301274
95-th percentile18.909053
Maximum83.606519
Range83.606519
Interquartile range (IQR)2.9140314

Descriptive statistics

Standard deviation8.4947977
Coefficient of variation (CV)2.4416999
Kurtosis38.157454
Mean3.4790508
Median Absolute Deviation (MAD)1.3555063
Skewness5.4846029
Sum1238.5421
Variance72.161589
MonotonicityNot monotonic
2024-04-07T22:46:49.881114image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2843556 1
 
0.1%
2.4224529 1
 
0.1%
2.5321083 1
 
0.1%
2.83466 1
 
0.1%
2.5080232 1
 
0.1%
1.5967027 1
 
0.1%
0.008686757126 1
 
0.1%
2.1665719 1
 
0.1%
0.004849622805 1
 
0.1%
3.2343489 1
 
0.1%
Other values (346) 346
 
17.7%
(Missing) 1594
81.7%
ValueCountFrequency (%)
0 1
0.1%
0.0001664096064 1
0.1%
0.0004438904029 1
0.1%
0.0004696946969 1
0.1%
0.000676382329 1
0.1%
0.0007468 1
0.1%
0.0009132956653 1
0.1%
0.0009655601197 1
0.1%
0.0011286 1
0.1%
0.001172784511 1
0.1%
ValueCountFrequency (%)
83.60651888 1
0.1%
71.649843 1
0.1%
47.88811129 1
0.1%
45.6118803 1
0.1%
36.63887939 1
0.1%
34.86947944 1
0.1%
29.52848758 1
0.1%
29.12461776 1
0.1%
27.52026395 1
0.1%
26.96119419 1
0.1%

sample_size_latest
Real number (ℝ)

MISSING 

Distinct302
Distinct (%)86.5%
Missing1601
Missing (%)82.1%
Infinite0
Infinite (%)0.0%
Mean1169.0083
Minimum2
Maximum110162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-04-07T22:46:49.930445image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile13.742
Q1105.8527
median197.6397
Q3402.5647
95-th percentile3117.377
Maximum110162
Range110160
Interquartile range (IQR)296.712

Descriptive statistics

Standard deviation7375.4926
Coefficient of variation (CV)6.3091875
Kurtosis169.96009
Mean1169.0083
Median Absolute Deviation (MAD)127.6397
Skewness12.609181
Sum407983.9
Variance54397891
MonotonicityNot monotonic
2024-04-07T22:46:49.980888image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112 5
 
0.3%
70 4
 
0.2%
189 4
 
0.2%
15 4
 
0.2%
8 4
 
0.2%
49 3
 
0.2%
196 3
 
0.2%
84 3
 
0.2%
14 3
 
0.2%
42 3
 
0.2%
Other values (292) 313
 
16.1%
(Missing) 1601
82.1%
ValueCountFrequency (%)
2 2
0.1%
4 1
 
0.1%
5 1
 
0.1%
5.57 1
 
0.1%
6 3
0.2%
7 1
 
0.1%
8 4
0.2%
10 1
 
0.1%
11 2
0.1%
13 1
 
0.1%
ValueCountFrequency (%)
110162 1
0.1%
77398 1
0.1%
18871 1
0.1%
18292 1
0.1%
11641 1
0.1%
9158 1
0.1%
7495 1
0.1%
6808 1
0.1%
5348.9975 1
0.1%
5108 1
0.1%

lag_latest
Real number (ℝ)

Distinct344
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.085641
Minimum1
Maximum734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-04-07T22:46:50.031987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median41
Q3129
95-th percentile322.85
Maximum734
Range733
Interquartile range (IQR)128

Descriptive statistics

Standard deviation119.42815
Coefficient of variation (CV)1.4203157
Kurtosis7.4121186
Mean84.085641
Median Absolute Deviation (MAD)40
Skewness2.4378187
Sum163967
Variance14263.084
MonotonicityNot monotonic
2024-04-07T22:46:50.081436image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 501
25.7%
5 124
 
6.4%
2 80
 
4.1%
3 56
 
2.9%
73 20
 
1.0%
4 20
 
1.0%
64 12
 
0.6%
12 12
 
0.6%
97 12
 
0.6%
49 11
 
0.6%
Other values (334) 1102
56.5%
ValueCountFrequency (%)
1 501
25.7%
2 80
 
4.1%
3 56
 
2.9%
4 20
 
1.0%
5 124
 
6.4%
6 10
 
0.5%
7 3
 
0.2%
8 7
 
0.4%
9 6
 
0.3%
10 7
 
0.4%
ValueCountFrequency (%)
734 1
0.1%
732 1
0.1%
725 1
0.1%
719 1
0.1%
711 1
0.1%
707 1
0.1%
692 1
0.1%
687 1
0.1%
679 1
0.1%
673 1
0.1%
Distinct1863
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6117274 × 109
Minimum1.5893888 × 109
Maximum1.6611971 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-04-07T22:46:50.134549image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.5893888 × 109
5-th percentile1.5983709 × 109
Q11.6041087 × 109
median1.6085043 × 109
Q31.6197766 × 109
95-th percentile1.6317247 × 109
Maximum1.6611971 × 109
Range71808300
Interquartile range (IQR)15667903

Descriptive statistics

Standard deviation11588509
Coefficient of variation (CV)0.0071901174
Kurtosis1.6863185
Mean1.6117274 × 109
Median Absolute Deviation (MAD)5778162
Skewness1.0270581
Sum3.1428684 × 1012
Variance1.3429354 × 1014
MonotonicityNot monotonic
2024-04-07T22:46:50.188020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1604109395 3
 
0.2%
1598372949 3
 
0.2%
1623163921 3
 
0.2%
1604113707 3
 
0.2%
1610896544 3
 
0.2%
1604108064 2
 
0.1%
1620399112 2
 
0.1%
1609729455 2
 
0.1%
1604108068 2
 
0.1%
1598370038 2
 
0.1%
Other values (1853) 1925
98.7%
ValueCountFrequency (%)
1589388807 1
0.1%
1589388916 1
0.1%
1589388926 1
0.1%
1589388997 1
0.1%
1589389060 1
0.1%
1589389166 1
0.1%
1590166272 1
0.1%
1590166275 1
0.1%
1591658520 1
0.1%
1592347926 1
0.1%
ValueCountFrequency (%)
1661197107 1
0.1%
1661197099 1
0.1%
1661197064 1
0.1%
1661196699 1
0.1%
1661196560 1
0.1%
1661196558 1
0.1%
1661196554 1
0.1%
1661196489 1
0.1%
1661196282 1
0.1%
1661196211 1
0.1%

computation_as_of_dt_latest
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1950
Missing (%)100.0%
Memory size15.4 KiB

missing_value_latest
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
0
1950 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1950
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1950
100.0%

Length

2024-04-07T22:46:50.235429image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T22:46:50.275519image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1950
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1950
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1950
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1950
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1950
100.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
5
1594 
0
356 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1950
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row0
3rd row0
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 1594
81.7%
0 356
 
18.3%

Length

2024-04-07T22:46:50.308047image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T22:46:50.349080image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
5 1594
81.7%
0 356
 
18.3%

Most occurring characters

ValueCountFrequency (%)
5 1594
81.7%
0 356
 
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 1594
81.7%
0 356
 
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 1594
81.7%
0 356
 
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 1594
81.7%
0 356
 
18.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
5
1601 
0
349 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1950
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row0
3rd row0
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 1601
82.1%
0 349
 
17.9%

Length

2024-04-07T22:46:50.385874image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T22:46:50.427015image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
5 1601
82.1%
0 349
 
17.9%

Most occurring characters

ValueCountFrequency (%)
5 1601
82.1%
0 349
 
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 1601
82.1%
0 349
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 1601
82.1%
0 349
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 1601
82.1%
0 349
 
17.9%

issue_update
Real number (ℝ)

SKEWED 

Distinct372
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20195426
Minimum202107
Maximum20220822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-04-07T22:46:50.472148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum202107
5-th percentile20200825
Q120201030
median20201220
Q320210428
95-th percentile20210915
Maximum20220822
Range20018715
Interquartile range (IQR)9398

Descriptive statistics

Standard deviation453018.87
Coefficient of variation (CV)0.022431756
Kurtosis1949.5337
Mean20195426
Median Absolute Deviation (MAD)596.5
Skewness-44.150888
Sum3.9381081 × 1010
Variance2.052261 × 1011
MonotonicityNot monotonic
2024-04-07T22:46:50.591794image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201030 289
 
14.8%
20200825 121
 
6.2%
20201014 118
 
6.1%
20201112 63
 
3.2%
20201017 49
 
2.5%
20210106 43
 
2.2%
20200623 27
 
1.4%
20210916 24
 
1.2%
20210221 23
 
1.2%
20210907 23
 
1.2%
Other values (362) 1170
60.0%
ValueCountFrequency (%)
202107 1
 
0.1%
20200514 6
 
0.3%
20200523 2
 
0.1%
20200528 1
 
0.1%
20200609 1
 
0.1%
20200617 1
 
0.1%
20200620 9
 
0.5%
20200623 27
1.4%
20200630 1
 
0.1%
20200701 1
 
0.1%
ValueCountFrequency (%)
20220822 15
0.8%
20220812 4
 
0.2%
20210930 3
 
0.2%
20210929 3
 
0.2%
20210928 6
 
0.3%
20210927 5
 
0.3%
20210926 6
 
0.3%
20210925 2
 
0.1%
20210924 3
 
0.2%
20210923 6
 
0.3%

value_update
Real number (ℝ)

SKEWED  ZEROS 

Distinct1288
Distinct (%)66.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean978.05284
Minimum-2.1428571
Maximum340882.01
Zeros456
Zeros (%)23.4%
Negative7
Negative (%)0.4%
Memory size15.4 KiB
2024-04-07T22:46:50.646517image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-2.1428571
5-th percentile0
Q10.040093664
median2.2548675
Q350.192292
95-th percentile5342.009
Maximum340882.01
Range340884.15
Interquartile range (IQR)50.152198

Descriptive statistics

Standard deviation9120.4606
Coefficient of variation (CV)9.3251205
Kurtosis1051.0108
Mean978.05284
Median Absolute Deviation (MAD)2.2548675
Skewness30.190171
Sum1907203
Variance83182801
MonotonicityNot monotonic
2024-04-07T22:46:50.698395image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 456
 
23.4%
1 29
 
1.5%
0.1428571 19
 
1.0%
0.1428571429 18
 
0.9%
2 17
 
0.9%
0.2857142857 10
 
0.5%
0.4285714 8
 
0.4%
0.7142857 8
 
0.4%
4 7
 
0.4%
5 7
 
0.4%
Other values (1278) 1371
70.3%
ValueCountFrequency (%)
-2.142857143 2
 
0.1%
-1.581327683 1
 
0.1%
-0.8571429 1
 
0.1%
-0.7113336795 1
 
0.1%
-0.2857143 1
 
0.1%
-0.2857142857 1
 
0.1%
0 456
23.4%
0.005062965267 1
 
0.1%
0.007039712802 1
 
0.1%
0.0160253 1
 
0.1%
ValueCountFrequency (%)
340882.0109 1
0.1%
167947.1874 1
0.1%
81852.71429 1
0.1%
30070.67938 1
0.1%
24169.42857 1
0.1%
22624 1
0.1%
18976 1
0.1%
17487.12805 1
0.1%
17082.48887 1
0.1%
16950.71396 1
0.1%

stderr_update
Real number (ℝ)

MISSING 

Distinct356
Distinct (%)100.0%
Missing1594
Missing (%)81.7%
Infinite0
Infinite (%)0.0%
Mean3.4790508
Minimum0
Maximum83.606519
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-04-07T22:46:50.753346image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0017151554
Q10.01609597
median1.3661809
Q32.9301274
95-th percentile18.909053
Maximum83.606519
Range83.606519
Interquartile range (IQR)2.9140314

Descriptive statistics

Standard deviation8.4947977
Coefficient of variation (CV)2.4416999
Kurtosis38.157454
Mean3.4790508
Median Absolute Deviation (MAD)1.3555063
Skewness5.4846029
Sum1238.5421
Variance72.161589
MonotonicityNot monotonic
2024-04-07T22:46:50.802667image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2843556 1
 
0.1%
2.4224529 1
 
0.1%
2.5321083 1
 
0.1%
2.83466 1
 
0.1%
2.5080232 1
 
0.1%
1.5967027 1
 
0.1%
0.008686757126 1
 
0.1%
2.1665719 1
 
0.1%
0.004849622805 1
 
0.1%
3.2343489 1
 
0.1%
Other values (346) 346
 
17.7%
(Missing) 1594
81.7%
ValueCountFrequency (%)
0 1
0.1%
0.0001664096064 1
0.1%
0.0004438904029 1
0.1%
0.0004696946969 1
0.1%
0.000676382329 1
0.1%
0.0007468 1
0.1%
0.0009132956653 1
0.1%
0.0009655601197 1
0.1%
0.0011286 1
0.1%
0.001172784511 1
0.1%
ValueCountFrequency (%)
83.60651888 1
0.1%
71.649843 1
0.1%
47.88811129 1
0.1%
45.6118803 1
0.1%
36.63887939 1
0.1%
34.86947944 1
0.1%
29.52848758 1
0.1%
29.12461776 1
0.1%
27.52026395 1
0.1%
26.96119419 1
0.1%

sample_size_update
Real number (ℝ)

MISSING 

Distinct302
Distinct (%)86.5%
Missing1601
Missing (%)82.1%
Infinite0
Infinite (%)0.0%
Mean1169.0083
Minimum2
Maximum110162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-04-07T22:46:50.852993image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile13.742
Q1105.8527
median197.6397
Q3402.5647
95-th percentile3117.377
Maximum110162
Range110160
Interquartile range (IQR)296.712

Descriptive statistics

Standard deviation7375.4926
Coefficient of variation (CV)6.3091875
Kurtosis169.96009
Mean1169.0083
Median Absolute Deviation (MAD)127.6397
Skewness12.609181
Sum407983.9
Variance54397891
MonotonicityNot monotonic
2024-04-07T22:46:50.903332image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112 5
 
0.3%
70 4
 
0.2%
189 4
 
0.2%
15 4
 
0.2%
8 4
 
0.2%
49 3
 
0.2%
196 3
 
0.2%
84 3
 
0.2%
14 3
 
0.2%
42 3
 
0.2%
Other values (292) 313
 
16.1%
(Missing) 1601
82.1%
ValueCountFrequency (%)
2 2
0.1%
4 1
 
0.1%
5 1
 
0.1%
5.57 1
 
0.1%
6 3
0.2%
7 1
 
0.1%
8 4
0.2%
10 1
 
0.1%
11 2
0.1%
13 1
 
0.1%
ValueCountFrequency (%)
110162 1
0.1%
77398 1
0.1%
18871 1
0.1%
18292 1
0.1%
11641 1
0.1%
9158 1
0.1%
7495 1
0.1%
6808 1
0.1%
5348.9975 1
0.1%
5108 1
0.1%

lag_update
Real number (ℝ)

Distinct344
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.085641
Minimum1
Maximum734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-04-07T22:46:50.956322image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median41
Q3129
95-th percentile322.85
Maximum734
Range733
Interquartile range (IQR)128

Descriptive statistics

Standard deviation119.42815
Coefficient of variation (CV)1.4203157
Kurtosis7.4121186
Mean84.085641
Median Absolute Deviation (MAD)40
Skewness2.4378187
Sum163967
Variance14263.084
MonotonicityNot monotonic
2024-04-07T22:46:51.006501image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 501
25.7%
5 124
 
6.4%
2 80
 
4.1%
3 56
 
2.9%
73 20
 
1.0%
4 20
 
1.0%
64 12
 
0.6%
12 12
 
0.6%
97 12
 
0.6%
49 11
 
0.6%
Other values (334) 1102
56.5%
ValueCountFrequency (%)
1 501
25.7%
2 80
 
4.1%
3 56
 
2.9%
4 20
 
1.0%
5 124
 
6.4%
6 10
 
0.5%
7 3
 
0.2%
8 7
 
0.4%
9 6
 
0.3%
10 7
 
0.4%
ValueCountFrequency (%)
734 1
0.1%
732 1
0.1%
725 1
0.1%
719 1
0.1%
711 1
0.1%
707 1
0.1%
692 1
0.1%
687 1
0.1%
679 1
0.1%
673 1
0.1%
Distinct1863
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6117274 × 109
Minimum1.5893888 × 109
Maximum1.6611971 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-04-07T22:46:51.061216image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.5893888 × 109
5-th percentile1.5983709 × 109
Q11.6041087 × 109
median1.6085043 × 109
Q31.6197766 × 109
95-th percentile1.6317247 × 109
Maximum1.6611971 × 109
Range71808300
Interquartile range (IQR)15667903

Descriptive statistics

Standard deviation11588509
Coefficient of variation (CV)0.0071901174
Kurtosis1.6863185
Mean1.6117274 × 109
Median Absolute Deviation (MAD)5778162
Skewness1.0270581
Sum3.1428684 × 1012
Variance1.3429354 × 1014
MonotonicityNot monotonic
2024-04-07T22:46:51.117428image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1604109395 3
 
0.2%
1598372949 3
 
0.2%
1623163921 3
 
0.2%
1604113707 3
 
0.2%
1610896544 3
 
0.2%
1604108064 2
 
0.1%
1620399112 2
 
0.1%
1609729455 2
 
0.1%
1604108068 2
 
0.1%
1598370038 2
 
0.1%
Other values (1853) 1925
98.7%
ValueCountFrequency (%)
1589388807 1
0.1%
1589388916 1
0.1%
1589388926 1
0.1%
1589388997 1
0.1%
1589389060 1
0.1%
1589389166 1
0.1%
1590166272 1
0.1%
1590166275 1
0.1%
1591658520 1
0.1%
1592347926 1
0.1%
ValueCountFrequency (%)
1661197107 1
0.1%
1661197099 1
0.1%
1661197064 1
0.1%
1661196699 1
0.1%
1661196560 1
0.1%
1661196558 1
0.1%
1661196554 1
0.1%
1661196489 1
0.1%
1661196282 1
0.1%
1661196211 1
0.1%

computation_as_of_dt_update
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1950
Missing (%)100.0%
Memory size15.4 KiB

missing_value_update
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
0
1950 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1950
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1950
100.0%

Length

2024-04-07T22:46:51.166529image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T22:46:51.206200image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1950
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1950
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1950
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1950
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1950
100.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
5
1594 
0
356 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1950
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row0
3rd row0
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 1594
81.7%
0 356
 
18.3%

Length

2024-04-07T22:46:51.239327image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T22:46:51.280285image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
5 1594
81.7%
0 356
 
18.3%

Most occurring characters

ValueCountFrequency (%)
5 1594
81.7%
0 356
 
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 1594
81.7%
0 356
 
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 1594
81.7%
0 356
 
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 1594
81.7%
0 356
 
18.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
5
1601 
0
349 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1950
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row0
3rd row0
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 1601
82.1%
0 349
 
17.9%

Length

2024-04-07T22:46:51.316103image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T22:46:51.356996image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
5 1601
82.1%
0 349
 
17.9%

Most occurring characters

ValueCountFrequency (%)
5 1601
82.1%
0 349
 
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 1601
82.1%
0 349
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 1601
82.1%
0 349
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1950
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 1601
82.1%
0 349
 
17.9%

Interactions

2024-04-07T22:46:48.142211image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:38.452615image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2024-04-07T22:46:40.566737image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2024-04-07T22:46:42.008991image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:42.609877image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:43.298310image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2024-04-07T22:46:44.711159image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2024-04-07T22:46:41.466824image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:42.126613image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2024-04-07T22:46:43.431477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2024-04-07T22:46:39.120391image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:39.783420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:40.469581image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:41.234434image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:41.913743image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:42.528187image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:43.208097image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:43.871264image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:44.616294image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:45.303777image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:45.996208image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:46.687741image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:47.319278image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:47.979802image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:48.786734image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:39.163856image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:39.828313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:40.517557image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:41.282211image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:41.960392image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:42.568369image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:43.250834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:43.916706image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:44.663278image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:45.350971image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:46.043371image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:46.729338image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:47.361367image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-07T22:46:48.093093image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Missing values

2024-04-07T22:46:48.867693image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-07T22:46:49.002813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-07T22:46:49.087853image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

signal_key_idgeo_key_idtime_typetime_valueissue_latestvalue_lateststderr_latestsample_size_latestlag_latestvalue_updated_timestamp_latestcomputation_as_of_dt_latestmissing_value_latestmissing_stderr_latestmissing_sample_size_latestissue_updatevalue_updatestderr_updatesample_size_updatelag_updatevalue_updated_timestamp_updatecomputation_as_of_dt_updatemissing_value_updatemissing_stderr_updatemissing_sample_size_update
01250day20220512202208220.871166NaNNaN1021661196211NaN055202208220.871166NaNNaN1021661196211NaN055
1178610day20190818202208020.1039810.00689684.05071609973490NaN000202101060.1039810.00689684.05071609973490NaN000
21764741day20190613202208110.0488020.00505349.05731609973321NaN000202101060.0488020.00505349.05731609973321NaN000
3292121day20210313202103169.285714NaNNaN31615942270NaN055202103169.285714NaNNaN31615942270NaN055
4961577day202103062021030870.463985NaNNaN21615229122NaN0552021030870.463985NaNNaN21615229122NaN055
569817day20200421202010300.571429NaNNaN1921604108117NaN055202010300.571429NaNNaN1921604108117NaN055
6731088day20210726202208030.142857NaNNaN11627392747NaN055202107270.142857NaNNaN11627392747NaN055
71044789day20200613202010173.955070NaNNaN1261602950850NaN055202010173.955070NaNNaN1261602950850NaN055
868981day20210302202208155329.938941NaNNaN11614783061NaN055202103035329.938941NaNNaN11614783061NaN055
9461918day202005122022080638.000000NaNNaN1051598371292NaN0552020082538.000000NaNNaN1051598371292NaN055
signal_key_idgeo_key_idtime_typetime_valueissue_latestvalue_lateststderr_latestsample_size_latestlag_latestvalue_updated_timestamp_latestcomputation_as_of_dt_latestmissing_value_latestmissing_stderr_latestmissing_sample_size_latestissue_updatevalue_updatestderr_updatesample_size_updatelag_updatevalue_updated_timestamp_updatecomputation_as_of_dt_updatemissing_value_updatemissing_stderr_updatemissing_sample_size_update
19404683day20200713202208203346.000000NaNNaN931602729075NaN055202010143346.000000NaNNaN931602729075NaN055
1941819798day20220509202208220.995000NaNNaN1051661196489NaN055202208220.995000NaNNaN1051661196489NaN055
19421771041day2019052620220818469.37500021.22396956.0005911609973274NaN00020210106469.37500021.22396956.0005911609973274NaN000
1943491096day20200414202208150.000000NaNNaN1331598370160NaN055202008250.000000NaNNaN1331598370160NaN055
19443811658day202205282022082252.9867374.667591114.341861661196282NaN0002022082252.9867374.667591114.341861661196282NaN000
1945972163day20200511202010170.000000NaNNaN1591602950499NaN055202010170.000000NaNNaN1591602950499NaN055
1946684653day20210718202208019844.402259NaNNaN11626688300NaN055202107199844.402259NaNNaN11626688300NaN055
19471034516day20200518202208194.000000NaNNaN1521602950571NaN055202010174.000000NaNNaN1521602950571NaN055
1948922709day20210326202208029149.392423NaNNaN21616958191NaN055202103289149.392423NaNNaN21616958191NaN055
19494199day20200528202011120.000000NaNNaN1681605173742NaN055202011120.000000NaNNaN1681605173742NaN055